Ovarian Image Processing for Diagnosis of a Subject

ABSTRACT

Embodiments relate to digital image processing for diagnosis of a subject. More specifically, the embodiments relate to automation of diagnoses through data interpretation. An image is acquired from the subject. Elements are recognized within the image based on morphological features. The image is compared to learned data. Based on the comparison, a probability of a potential diagnosis(es) is calculated. A diagnosis of the subject is determined based on the potential diagnosis(es) and the calculated probability. The diagnosis may be changed based on a new image acquired from the subject.

BACKGROUND

The present embodiments relate to digital data processing for diagnosisof a subject. More specifically, the embodiments relate to automation ofdiagnoses through data interpretation.

The medical field continues to prefer non-invasive procedures todetermine indicators for pathology identification and normalcyconfirmation. Non-invasive procedures provide optimal subject comfortand limit harmful side effects. One popular non-invasive procedure ismedical imaging of the subject, such as x-rays, ultrasound, and magneticresonance imaging (MRI). The use of ultrasound and MRI technologymitigates or eliminates exposure to radiation while producing images ofinternal organs. For example, ultrasound imaging is used to produceimages of internal organs of a subject by using sound waves. It isunderstood that medical images are subject to interpretation that isgenerally conducted by a trained Radiologist. Accordingly, imagingtechniques are dependent on manual inspection and analysis which causessevere time overheads and are subject to interpretation which can leadto differences in diagnosis.

Another non-invasive medical procedure is electroencephalography (EEG),which is used to determine electrical activity of the brain to helpdiagnose epilepsy and forecast seizures. Forecasting seizures isimportant in order to warn the subject to avoid potentially dangerousactivities, such as driving. Similar to medical imaging techniques, theEEG output is also subject to interpretation by a medical professional,which leads to varying analysis, and in one embodiment predictions. Forexample, in order to predict the seizures throughout the day, the EEGwould have to be constantly monitored leading to an unmanageable timeoverhead.

From a health perspective, non-invasive procedures and medicalevaluation processes, such as medical imaging and EEGs, may be preferredfor diagnosis of a subject. However, as articulated above, suchprocedures, and more specifically, the output of such procedures issubject to interpretation. Any time a person is involved in suchinterpretations there is overhead and subjectivity, with overheadleading to an increase in cost(s), and subjectivity leading to perhapsdifferent diagnoses.

SUMMARY

A system, computer program product, and method are provided to digitallyprocess data in support of a medical diagnosis.

In one aspect, a computer system is provided with a processing unit incommunication with a memory, and a functional unit in communication withthe processing unit. The functional tool has an image diagnostic tool todetermine a diagnosis. The image diagnostic tool captures an image of anovary and within the image a follicle is recognized using amorphological feature of the follicle. A template set is used torecognize each morphological feature on a pixel basis. Morespecifically, the template set has an instruction for determination ofone or more morphological features. The recognized follicle is comparedto a previously recognized follicle of at least one ovary on a pixelbasis. The image diagnostic tool determines a probability based on thecomparison. The comparison and probability are used to create adiagnosis. The image diagnostic tool uses the recognized follicle,diagnosis, and probability to convert the image to a diagnostic image.More specifically, the diagnostic image is a visual representation ofthe diagnosis.

In another aspect, a computer program product is provided to determine adiagnosis. The computer program product includes a computer readablestorage device with embodied program code that is configured to beexecuted by a processing unit. More specifically, program code isprovided to capture an image of an ovary and within the image a follicleis recognized using a morphological feature of the follicle. A templateset is used to recognize each morphological feature on a pixel basis.More specifically, the template set has an instruction for determinationof one or more morphological features. The recognized follicle iscompared to a previously recognized follicle of at least one ovary on apixel basis. The program code determines a probability based on thecomparison. The comparison and probability are used to create adiagnosis. The program code uses the recognized follicle, diagnosis, andprobability to convert the image to a diagnostic image. Morespecifically, the diagnostic image is a visual representation of thediagnosis.

In yet another aspect, a method to is provided to determine a diagnosis.An image of an ovary is captured and within the image, and a follicle isrecognized using a morphological feature of the follicle. A template setis used to recognize each morphological feature on a pixel basis. Morespecifically, the template set has an instruction for determination ofone or more morphological features. The recognized follicle is comparedto a previously recognized follicle of at least one ovary on a pixelbasis. The comparison is used to determine a probability. The comparisonand probability are used to create a diagnosis. The recognized follicle,diagnosis, and probability are used to convert the image to a diagnosticimage. More specifically, the diagnostic image is a visualrepresentation of the diagnosis.

These and other features and advantages will become apparent from thefollowing detailed description of the presently preferred embodiment(s),taken in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The subject matter which is regarded as embodiments is particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The forgoing and other features, and advantages ofthe embodiments are apparent from the following detailed descriptiontaken in conjunction with the accompanying drawings in which:

FIG. 1 depicts a flow chart illustrating an embodiment of interpretingan image based on learned data.

FIG. 2 depicts a flow chart illustrating an embodiment of diagnosing asubject utilizing multiple images acquired at different points in time.

FIG. 3 depicts a flow chart illustrating a determination of trend(s) ofmorphological feature(s) over time.

FIG. 4 depicts a flow chart illustrating a determination of a polylinediagnosis of a subject.

FIG. 5 depicts a flow cart illustrating an embodiment for determining adiagnosis of an optimal time for insemination of a subject.

FIG. 6 depicts a flow chart illustrating an embodiment for determiningan optimal time for insemination based on a time series analysis of asubject.

FIG. 7 depicts a flow chart illustrating an embodiment for determining adiagnosis of diabetic retinopathy based on a retinal scan.

FIG. 8 depicts a flow chart illustrating an embodiment for determining adiagnosis of a seizure based on a brain wave scan.

FIG. 9 is a block diagram illustrating an example of a computersystem/server of a cloud based support system, to implement the processdescribed above with respect to FIGS. 1-8.

FIG. 10 depicts a block diagram illustrating a cloud computerenvironment.

FIG. 11 depicts a block diagram illustrating a set of functionalabstraction model layers provided by the cloud computing environment.

DETAILED DESCRIPTION

It will be readily understood that the components of the presentembodiments, as generally described and illustrated in the Figuresherein, may be arranged and designed in a wide variety of differentconfigurations. Thus, the following detailed description of theembodiments of the apparatus, system, and method of the presentembodiments, as presented in the Figures, is not intended to limit thescope of the embodiments, as claimed, but is merely representative ofselected embodiments.

Reference throughout this specification to “a select embodiment,” “oneembodiment,” or “an embodiment” means that a particular feature,structure, or characteristic described in connection with the embodimentis included in at least one embodiment of the present embodiments. Thus,appearances of the phrases “a select embodiment,” “in one embodiment,”or “in an embodiment” in various places throughout this specificationare not necessarily referring to the same embodiment.

The illustrated embodiments will be best understood by reference to thedrawings, wherein like parts are designated by like numerals throughout.The following description is intended only by way of example, and simplyillustrates certain selected embodiments of devices, systems, andprocesses that are consistent with the embodiments as claimed herein.

A system, method and computer program product are provided to digitallyprocess data in support of a medical diagnosis. More specifically, theembodiments relate to interpreting a series of data, or in oneembodiment a plurality of images, acquired over time and providing adiagnosis or recommendation based on the interpretation. The embodimentsrelate to employing machine learning into the interpretation process andto automate interpretation of data in order to provide accurateinterpretation output.

Data may be, but is not limited to, data acquired from one or moresensors and images. Medical sensor data may be acquired from, but is notlimited to, an EEG. Similarly, medical image data may be acquired from,but is not limited to, one or more ultrasound images, an x-ray, one ormore Magnetic Resonance Imaging (MRI) images, a Positron emissiontomography (PET) Scan, a Single-photon emission computed tomography(SPECT) scan, an Optical coherence tomography (OCT) scan, one or moreelastography images, one or more tactile images, one or morephotoacoustic images, one or more thermographic images, anechocardiogram, one or more funduscopic images, a renal scan, and one ormore functional near-infrared spectroscopic images.

Referring to FIG. 1, a flow chart (100) is provided illustrating anembodiment of interpreting one or more images or medical related imagesbased on machine learning. As shown, an image is acquired from a subjectbased on a predetermined region of interest at a first point-in-time(102). The region of interest is selected based on at least onepredetermined element which relates to a probable indicator for adiagnosis. In one embodiment, the element will be tracked over time inorder to determine changes and indicators of a condition in order toprovide an accurate diagnosis. The element may be, but is not limitedto, a dominant follicle, recessive follicle, ovary, vasculature, chambersize, fat, water, retina, lens, brain, muscle, tendon, ligament, or inone embodiment any other element that may be imaged within the subject.For example, if a diagnosis of an optimal time for insemination isdesired, the region of interest would be the ovary(ies) and the trackedelement would be a dominant follicle wherein the tracking will providean indication of the proper time for insemination. Accordingly, a firstimage of a selected or designated element is acquired in order toacquire data for a diagnosis.

All images are subject to interpretation. Medical images are a class ofimages associated with interpreting a possible medical condition. Assuch, the interpretation may be considered of greater importance thanthat of a non-medical image. The image interpretation shown hereinincludes recognition of the selected or designated element within theimage utilizing at least one morphological feature of the element (104).The morphological feature of the element may be, but is not limited toedge, size, shape, length, width, angle, tortuosity, and, color. Therecognition utilizes learned data which contains other images associatedwith a similar region of interest of at least one previous subject, andin one embodiment at least one data file retained in data storage. Inone embodiment, the image(s) contained in the data storage that areutilized for the recognition at step (104) contain one or more similarelement(s) with at least one morphological feature for comparison.Additionally, the data file(s) employed or selected for the comparisonmay contain, but not limited to, an element definition (i.e. instructionon how to identify the element), morphological feature definition (i.e.instruction on how to identify the morphological feature), a templateset for image recognition, a diagnosis of a subject and subject data.The subject data may be, but is not limited to, age, weight, bloodpressure, glucose level, location, nationality, bodily configuration,medical history, or other data associated with the subject. Themorphological features of the element may be recognized by, but is notlimited to, a comparison to one or more stored image(s), morphologicaloperators, edge detection, use of a template set, and/or use ofinstructions. Based on the morphological feature(s), the element that isthe subject of the evaluation may be recognized and the morphologicalfeature(s) of the element may be tracked. Accordingly, the data storageprovides a resource for learned data for use in recognition of elementsin order to increase the accuracy of the recognition and evaluation.

In order to provide an accurate recognition of a morphological feature,the data storage may be filtered to limit images and data files withsimilar subject data. For example, in one embodiment, data of thecurrent subject may be used to eliminate at least one subject withdissimilar subject data. In one embodiment, the subject data may be usedto assign a weight to each image of a subject in the data storage usedfor comparison. Accordingly, subject data may be used for filtering thedata storage for comparison.

Following the recognition at step (104), the morphological feature(s) ofthe element is measured (106). Based on the recognition of the elementand measurement of the morphological feature(s), the first image iscompared to learned data, e.g. a previous image of a previous subject inthe data storage (108). The comparison includes determining one or moresimilarities and/or differences between the morphological feature(s) ofthe current subject versus the morphological feature(s) of the previoussubject(s), including similarity(ies) and/or difference(s) betweenmeasurement(s). Accordingly, the image of the current subject iscompared to learned image data in order to determine similarities anddifferences.

Following the comparison at step (108), a potential diagnosis(es) isdetermined based on the comparison, and a probability(ies) of thepotential diagnosis(es) is calculated based on the degree of similaritybetween the first image of the current subject and the image of acompared previous subject having the potential diagnosis (110). Theprobability is based on the degree of similarity between themorphological feature(s) of an element(s) within the first image and themorphological feature(s) of at least one image associated with thepotential diagnosis. In one embodiment, the subject data of the currentsubject is utilized to increase or decrease the probability of apotential diagnosis. In one embodiment, the subject data of the currentsubject is utilized to filter the data storage before determiningpotential diagnosis(es). Accordingly, probabilities of potentialdiagnosis are assessed as part of the image and associated featurecomparison.

After the calculation of the probability(ies) at step (110), a diagnosisof the current subject is determined (112). More specifically, thediagnosis is determined based on at least one potential diagnosis andthe calculated probability of the potential diagnosis(es). In oneembodiment, the diagnosis of the subject is based on the potentialdiagnosis with the highest probability. In one embodiment, the diagnosisof the subject is an aggregate of at least two diagnoses from at leasttwo potential diagnoses and selection of the aggregate diagnosis.Accordingly, the diagnosis is determined by using learned data as abasis for comparison and probability assessment.

Referring to FIG. 2, a flow chart (200) is provided illustrating anembodiment of diagnosing a subject utilizing multiple images acquired atdifferent points in time and creating a graph for performing a timeseries analysis of the data. As shown in FIG. 1, a diagnosis isdetermined. A first point on a graph is created based on the calculatedprobability of the diagnosis (202). The point at step (202) correspondsto the first point-in-time (X-dimension) that the first image wasacquired and the calculated probability of the diagnosis of the subject(Y-dimension). Accordingly, the graph entry created at step (202) servesas a basis to track the probability of a diagnosis of the subject overtime.

A second image is acquired from the subject at a second point-in-timebased on the pre-determined region of interest of the first image andprocess as shown and describe in FIG. 1, (204). In one embodiment,during recognition of the element in the second image a comparison tothe first image is performed. In one embodiment, the image processingshown and described at step (204) includes a diagnosis pertaining to thesecond image. Accordingly, in addition to the first image and firstdiagnosis, a second diagnosis is determined based on the second image.

In response to the determination of the second diagnosis, a second pointon the graph is created (206). The second point corresponds to thesecond point-in-time that the second image was acquired and theprobability of the diagnosis of the subject. Additionally, if thediagnosis for the second point is different than the diagnosis for thefirst point then a re-calculation of the first point will be performedwherein the re-calculation is based on the diagnosis for the secondpoint (208). Thus, the calculated probability for each point is based onthe same diagnosis(es).

After creation of the second point, a line is created connecting thefirst point to the second point, thus creating a polyline based on dataacquired from the subject (210). The polyline of the subject providestime dependent probability data based on a diagnosis of the subject andimages taken of the subject over time. As described above, the points onthe polyline are calculated based on the same diagnosis(es). Thepolyline can be re-calculated based on a different diagnosis. In oneembodiment, the re-calculation of the polyline can be performediteratively for different diagnoses until an optimal diagnosis isdetermined (212). The optimal diagnosis may be determined based oncomparing different iterations of the polyline based on one or moreparameters of the polyline. The one or more parameters may be, but isnot limited to, probability value(s) of one or more points, an areaunder or above the polyline, slope(s), curvature, length(s), startingpoint, ending point, horizontal relation, vertical relation, standarddeviation of two or more points, average of two or more points, medianof three or more points, and mode of three or more points. In oneembodiment, the optimal diagnosis is based on the one or more parametersand one or more respective threshold values. The quantity of iteration,images, or points should not be considered limiting. In one embodiment,the quantity may be based on a predetermined value. Further, a polylinemay be recalculated based on learned data acquired after the polylinewas created. Accordingly, the polyline is used to determine theprobability of a diagnosis based on a time series analysis of images inorder to find an optimal diagnosis for the subject.

Measurement trends of the morphological feature(s) show one or more timebased changes or similarities of the morphological feature. Referring toFIG. 3, a flow chart (300) is provided illustrating a determination oftrend(s) of morphological feature(s) over time. As shown, adetermination of difference(s) and similarity(ies) of the morphologicalfeature(s) of the tracked element(s) over time is performed (302). Thedifference(s) and similarity(ies) of the morphological feature(s) aredetermined and used to create a trend of the morphological feature(304). The trend(s) of the morphological feature(s) is compared tolearned data, e.g. a trend(s) of a morphological feature(s) of previoussubjects in the data storage. The trend(s) provide another form ofcomparison to learned data in order to achieve a more accurate diagnosisof the subject. Accordingly, a trend is created and used to determinesimilar trend(s) of corresponding or similar diagnosis(es).

The polyline created in FIG. 2 may be subject to further analysis andre-calculation. Referring to FIG. 4, a flow chart (400) is providedillustrating a determination of a polyline diagnosis of a subject. Asshown, a polyline probability is calculated, and in one embodimentre-calculated, using a probabilistic framework and risk analysisutilizing multiple dimensions in order to perform a comparison of theindividual time points and the polyline as a whole to learned data(402). A higher polyline probability value implies that the polyline ofthe subject has a high similarity to the compared diagnosis. Conversely,a lower polyline probability implies that there are fewer similaritiesto the compared diagnosis. Accordingly, the polyline is used todetermine the likelihood of a subject having a condition based on acomparison of data acquired from the subject over time to learned data.

In order to determine the polyline probability(ies) of potentialpolyline diagnosis(es), a weight is assigned to each morphologicalfeature and in one embodiment each time point. The weight may bedetermined from learned data, and in one embodiment examining storeddata. Calculating polyline probabilities includes substituting a rangeof values for each morphological feature measurement used to construct apoint on the polyline. In one embodiment, the measurement of eachmorphological feature at each time point has the measured valuesubstituted for a range of values depending on the measurement error.The calculation is performed iteratively, each iteration using adifferent set of random values as inputs to the probability functions ofeach point on the polyline until a predetermined level of certainty isachieved (404). The calculation may include but is not limited to, MonteCarlo simulations, Wavelet analysis, or Markov chains. Accordingly, apolyline probability is calculated in order to analyze the time seriesof acquired images.

A polyline diagnosis of the subject is determined utilizing the list ofpossible polyline diagnosis(es) (406). In one embodiment, the polylinediagnosis is the potential diagnosis with the highest probability value(406). In one embodiment, the polyline line diagnosis is used whenselecting a treatment to perform on a subject, and following theselection the treatment is performed. In one embodiment, the polyline isused by a physician or medical professional examining the subject as avisual aid in order to make a determination of a diagnosis and a propercourse of treatment to perform on the subject. Accordingly, a polylinediagnosis allows a determination of a diagnosis based on a probabilisticframework and risk analysis utilizing multiple dimensions of data.

In one embodiment, an aggregated case is created and stored forcomparison to subjects. An aggregated case is based on the data andimages acquired from a plurality of subjects. All subjects with asimilar diagnosis may be aggregated into a single aggregated case. Theaggregated case provides a method to search stored data, compare data,process data, and calculate a probability(ies). The aggregated caseenables a determination of an accurate diagnosis of the subject sincethe aggregated case is based on multiple subjects with similar diagnosesinstead of a single subject. Additionally, the aggregated case maycontain aggregated images. The aggregated images are images createdbased on the images acquired from each subject that is a member of theaggregated case. Each morphological feature of each element is createdbased on the similarities and differences between each subject of theaggregated case. In one embodiment, the subjects used to create anaggregated case have similar subject data. Further, the aggregated casemay contain an aggregated template for image analysis. Accordingly, anaggregated case is created to summarize learned data for an accuratediagnosis and more efficient search of the stored data.

As described herein, ultrasound images may be used for reproductivestudies to diagnose an optimal time for insemination of a subject. Inorder to determine the optimal time, a complete understanding of theovarian follicle dynamics is crucial. One skilled in the art wouldrecognize that the dominant follicle(s) which have the potential toovulate must be identified. Not all dominant follicles ovulate, and notall ovulating dominant follicles are of sufficient quality to result inpregnancy. As such, the dominate follicles must be characterized. Thecharacterization can occur through a comparison between dominantfollicles resulting in pregnancy, and those less dominant follicles thatdid not result in pregnancy. Accordingly, comparison of dominantfollicles is crucial in order to provide an accurate diagnosis of anoptimal time for insemination.

It is understood that during image processing, follicles are difficultto characterize even though, basic image processing steps are requiredfor their recognition (i.e. preprocessing, segmentation, featureextraction and classification). Features such as blood vessels,lymphatic glands, and nerve fibers present in ultrasound images causeissues with recognizing particular element(s). Further, speckle noiseinherent to ultrasound imaging inhibits element recognition. A cellularneural network (CNN) coupled with learned data is employed to improvethe follicle recognition and mitigate the inherent issues associatedwith ultrasound imaging and processing.

A CNN constitutes a class of recurrent and locally coupled arrays ofsimple processing elements (cells). In a CNN, information is directlyexchanged between neighboring cells allowing for increased learning ofrepresentations of key elements and their corresponding morphologicalfeatures. The connectivity among the cells is determined using a set ofparameters denoted as a template set, which can be initialized based onthe template set stored in data storage. The template set can bedynamically updated based on newly acquired data. The template setprovides a method for the CNN to learn thereby providing more efficientrecognition of elements and an accurate diagnosis(es). The CNNarchitecture provided for follicle detection utilizes two sequentiallyconnected CNNs wherein each image pixel links to a CNN cell. The CNNarchitecture utilizes the learned data during element recognition, whichprovides improved preprocessing, segmentation and recognition within theacquired image. Accordingly, a deep CNN architecture that preserves thetranslation in sequential frames, learns from these translations andrenders an improved representation is provided.

Referring to FIG. 5, a flow chart (500) is provided illustrating anembodiment of determining a diagnosis of an optimal time forinsemination based on an image of a subject. As shown, a first image ofa region encompassing at least one ovary of a subject at a firstpoint-in-time is acquired, utilizing a first CNN (502). The ovarycontains follicles which must be monitored and tracked in order todetermine the optimal time for insemination. Following acquisition ofthe image, one or more potential dominant follicle(s) are recognizedwithin the first image utilizing a morphological feature based on thetemplate set (504). In an ultrasound, the recognition utilizes themorphological feature of color including determining the darkestobject(s) in the image. In one embodiment, the recognition parameters(i.e. how to determine a threshold for “darkest”) are contained in thetemplate set. In one embodiment, the recognition parameters are storedas instruction(s) in data storage and are referenced in order todetermine elements in the image. In one embodiment, the template isupdated based on the recognition of the current image. Accordingly, thedarkest object(s) in the image are recognized as potential dominantfollicle(s).

The image created following step (504) is further processed utilizing asecond CNN (506). The processing in step (506) includes changing theregion of interest to encompass only a region sized with respect to thelocations of the potential dominant follicles found in step (504). Inone embodiment, the changing of the region is performed based onparameters in a template set. Accordingly, the region of interest ischanged to encompass only those elements found to be potential dominantfollicle(s).

The processed image output at step (506) and the original first imageare exploited by the two sequentially connected CNNs to recognize anovary, with the exploitation utilizing morphological features of theovary (508). In one embodiment, the ovary is recognized based onparameters in the template set. Phantom follicles are recognized andeliminated from the potential dominant follicle(s) (510). Phantomfollicles are not within the boundary of the ovary, thus the recognitionof phantom follicles utilizes the morphological feature of location. Anyfollicle having a location outside the boundary of the ovary is removedfrom potential dominant follicles. Accordingly, as shown herein thephantom follicles are removed from the potential dominant follicle(s).

The processed image output from step (510) is used to recognizerecessive follicle(s) and remove the recognized recessive follicle(s)from the potential dominant follicle(s) (512). The recognition isperformed only on the region of the image that contains the potentialdominant follicles. The recognition utilizes brightness of the image andlowers the bias of the image to determine recognized follicles over abrightness threshold and determine the recognized follicles over thethreshold are recessive follicle(s). The lowering of the bias limits theintensity gradient shown in the image. In one embodiment, the thresholdfor brightness and bias lowering parameters are adjustable. Similarly,in one embodiment, the threshold for brightness and bias loweringparameters are stored in the template set. Lowering the bias enables thesequentially connected CNNs to efficiently and accurately segment theimage. Accordingly, as shown, recessive follicles are determined,segmented, and removed from the potential dominant follicles.

Following step (512) autonomous follicles are recognized from theremaining potential dominate follicle(s) and designated as actualdominate follicle(s) (514). Dominant follicles do not connect twofollicles. Therefore, determining autonomous follicles provides theactual dominate follicle(s). The recognition utilizes boundaries of thepotential dominant follicles to determine if any potential dominantfollicle intersects with another potential dominant follicle. Anypotential dominant follicle which intersects two or more potentialdominant follicles is removed from designation as a potential dominantfollicle(s). Following any removals, the remaining potential dominantfollicle(s) are actual dominant follicle(s). In one embodiment, theprocedure for determining the actual dominant follicle is stored in thetemplate set. In one embodiment, the dominant follicles are segmented inthe image and used to create an interpretable image (516). Theinterpretable image may be used by a physician, or medical professional,examining the subject. Accordingly, the segmentation in the imagefunctions as a basis to interpret and can be used to provide adiagnosis(es).

Following the recognition of the dominant follicle(s), the morphologicalfeatures of the dominant follicles are measured (518). Such measuredfeatures include, but are not limited to, edge intensity, shape, size,and color. In one embodiment, gray-scale features and gray-scalegradient features are achieved through a wavelet analysis. The processedimage and measured morphological features are compared to one or morepreviously storage images and associated measurements (520). Thecomparison includes determining similarities and differences between thedominant follicles of the current subject versus the dominant folliclesof a previous subject(s) including similarities and differences betweenmeasurement(s). Accordingly, the recognized dominant follicles arecompared to the learned data.

Based on the comparison, a probability(ies) of a diagnosis(es) (i.e.optimal time to inseminate the subject) is calculated based on thedegree of similarity between the dominant follicles of the currentsubject and the dominant follicles of a compared previous subject (522).A potential time for insemination is determined from the stored databased on the comparison, and a probability of the time for inseminationcalculated based on a comparison of the dominant follicles. Theprobability is based on the degree of similarity between the dominantfollicle(s) of the current subject and the dominant follicle(s) of atleast one image associated with the potential diagnosis. After thecalculation of the probability(ies), the diagnosis of the optimal timefor insemination is determined (524). The diagnosis of the subject isdetermined based on at least one potential diagnosis. In one embodiment,the diagnosis of the subject is determined based on the potentialdiagnosis with the highest calculated probability. In one embodiment,the determined diagnosis and calculated probability are displayed on ornear the interpretable image and key morphological features (determinedfrom learned data) in the interpretable image used in making thediagnosis are highlighted or otherwise identified. Accordingly, theoptimal time for insemination is determined based on comparison tolearned data.

Referring to FIG. 6 a flow chart (600) is provided illustrating anembodiment for determining an optimal time for insemination based on atime series analysis of the subject. As shown, a first point on a graphis created based on the first point-in-time that the first image wasacquired and the calculated probability of the optimal time forinsemination (602). Accordingly, the graph is used to track theprobability of the optimal time for insemination over time.

Following initiation of the graph at step (602), data is acquired fromthe subject over time. For instance, a second image is acquired from thesubject at a second point-in-time based on the predetermined region ofinterest of the first image utilizing the steps as shown and describedin FIG. 5, (604). Following the acquisition, processing, segmentationand measurement of the second image a second point on the graph iscreated (606). The second point corresponds to the second point-in-timethat the second image was acquired and the probability of the optimaltime for insemination. All points on the graph correspond to the samediagnosis(es). Thus, if the diagnosis for the second point is differentthan the diagnosis for the first point then a re-calculation of thefirst point will be performed based on the diagnosis of the secondpoint. Accordingly, all points on the graph are calculated based on thesame diagnosis(es).

A line is created connecting the first point to the second pointcreating a polyline (608). Similar to FIG. 2 step (212), in oneembodiment, the re-calculation of the polyline can be performediteratively for different diagnoses until an optimal diagnosis isdetermined. The quantity of images acquired and points created on agraph should not be considered limiting. A polyline diagnosis of theoptimal time for insemination is determined based on a change in thedominate follicles as shown and described in FIG. 4, (610). In oneembodiment, a diagnosis image may be created which includes, thepolyline, the polyline diagnosis, the calculated polyline probability,and the interpretable image. In one embodiment, the diagnosis image isused by a physician, or other medical professional, examining thesubject as a visual aid in order to make a determination of a diagnosisand a proper course of treatment to perform on the subject. Accordingly,the polyline is used to determine the optimal time for insemination andthe probability of the optimal time for insemination.

In one embodiment, polycystic ovary syndrome (PCOS) may be detected.PCOS is a common hormonal disorder that causes enlarged ovariescontaining numerous cysts located across the outer edge of each ovary.PCOS detection acquires a series of transvaginal ultrasounds lastingover the entire menstrual cycle of a subject in order to monitorfollicular growth and capture the ovary's response when subjected tofollicle tracking treatments. Various morphological features can betracked in order to diagnosis PCOS such as, but not limited to, shape,antral edge quality, size and echogenicity. Further, the currentembodiments reduce the time overheads require by manual studying ofultrasound images thereby providing efficiency to the study anddiagnosis process.

In one embodiment, the evaluation and diagnosis prediction shown anddescribed above may be applied to detection of diabetic retinopathy.Diabetic retinopathy affects blood vessels in light-sensitive tissues,e.g. the retina, which lines the back of the eye. Referring to FIG. 7, aflow chart (700) is provided illustrating an embodiment of determining adiagnosis of diabetic retinopathy based on a retinal scan. Diabeticretinopathy detection acquires a retinal scan, e.g. Optical CoherenceTomography (OCT) scan, of a subject's eye at a first point in time(702). The retinal scan is processed in order to recognize bloodvessel(s) and/or floater(s), e.g. vitreous jelly that may start to formin the eye indicating damage (704). The recognition utilizesmorphological features of the blood vessel and floater such as, but notlimited to, diameter, length, branching angle, and tortuosity.Additionally, the recognition utilizes learned data which contains otherretinal scans associated with an eye of at least one previous subject.Accordingly, learned data is utilized in order to increase the accuracyof recognition of blood vessels and floaters for determining a diagnosisrelated to diabetic retinopathy.

The morphological feature(s) of the blood vessel and/or floater ismeasured (706). Based on the recognition of the blood vessel and/orfloater and measurement of their respective morphological feature, thefirst scan is compared to learned data, e.g. a previous retinal scan ofa previous subject in data storage (708). Following the comparison atstep (708), a potential diagnosis(es) is determined based on thecomparison, and a probability(ies) of the potential diagnosis(es) iscalculated based on the degree of similarity between the first retinalscan of the current subject and the compared retinal scan of a previoussubject having the potential diagnosis (710). After the calculation ofthe probability at step (710), a diagnosis of the current subject isdetermined (712). In one embodiment, an interpretable image may becreated from the retinal scan, determined probability, determineddiagnosis, and/or morphological feature(s). Accordingly, the diagnosisis determined by using learned data as a basis for comparison andprobability assessment.

In one embodiment, at least one additional retinal scan of the subjectis acquired at a second point in time to determine any changes in themorphological feature(s) from the first point in time to the secondpoint in time (714). The scan(s) is then processed (716) as shown anddescribed in steps (704)-(712). Change in the morphological feature(s)over time can indicate change(s) in the blood vessel(s), swelling andleaking, and floater(s) which are indicative of diabetic retinopathy.Based upon the first OCT scan and the second OCT scan a polylinediagnosis is created (718), as shown and described in FIGS. 2-4. Thequantity of scans and points in time the scans are acquired should notbe considered limiting. Accordingly, the processes shown and describedin FIGS. 1-4 may be expanded to the field of study of diabeticretinopathy by monitoring floaters and blood vessels in the eye throughassociated retinal scans.

In another embodiment, the process of image evaluation and diagnosis maybe applied to the detection of epileptic seizures. Referring to FIG. 8,a flow chart (800) is provided illustrating an embodiment of determininga diagnosis of a seizure based on a brain wave scan. Seizure detectionacquires a scan of a subject's brainwave, hereinafter “brain wave scan”,at a first point in time (802). For example, an EEG or anelectrocorticogram (ECoG) may be used to acquire a brain wave scan. TheEEG and ECoG provide data about the subject's brain waves, i.e voltagefluctuations, usually viewed as a graph. Features of the brain waves inthe graph are recognized (804) and measured (806). The recognitionutilizes learned data which contains other scan(s) associated with brainwave(s) of at least one previous subject. Accordingly, learned data isutilized in order to increase the accuracy of recognition of brain wavefeature(s) for determining a diagnosis related to seizures.

The brain wave feature(s) is compared to a data storage of learn datacontaining other brain wave scan(s) of a previous subject(s) and brainwave feature(s) indicative of a seizure (808). Following the comparisonat step (808), a potential diagnosis(es) is determined based on thecomparison, and a probability(ies) of the potential diagnosis(es) iscalculated based on the degree of similarity between the first brainwave scan of the current subject and a compared brain wave scan of aprevious subject having the potential diagnosis (810). After thecalculation of the probability at step (810), a diagnosis of the currentsubject is determined (812). In one embodiment, an interpretable imagemay be created from the brain wave scan (e.g EEG), determinedprobability, determined diagnosis, and/or morphological feature(s) andpresented on a display device. Accordingly, the diagnosis is determinedby using learned data as a basis for comparison and probabilityassessment.

In one embodiment, an additional brain wave scan of the subject isacquired at a second point in time to determine any changes in themorphological feature(s) from the first point in time to the secondpoint in time (814). The brain wave scan(s) is then processed (816) asshown and described in steps (804)-(812). Based upon the first brainwave scan and the second brain wave scan a polyline diagnosis is created(818) as shown and described in FIGS. 2-4. The quantity of brain wavescans and points in time the brain wave scans are acquired should not beconsidered limiting. In one embodiment, in order to determine a polylinediagnosis, a time series analysis of the brain wave scans is calculatedutilizing a Markov Chain to compute a multiple dimension jointprobability of a potential diagnosis(es) (i.e. time of next seizure).During the calculation, a weight is assigned to every characteristic inthe EEG signal that is deterministic of a seizure. After thecalculation, the time of subject's next seizure is determined, or in oneembodiment, projected, based on the potential time of the next seizurewith the highest probability. In one embodiment, CT scans and MRIs maybe acquired and monitored over time to determine abnormalities in thebrain for predicting seizures. Accordingly, the processes shown anddescribed in FIGS. 1-4 may be expanded to the field of study of seizuresby monitoring brain waves through associated brain wave scans, such asan EEG.

Aspects of interpreting a series of images acquired from a subject overtime and providing a diagnosis based on the interpretation provided inFIGS. 1-8, employ one or more functional tools to support use of aprediction pertaining to a medical condition. Aspects of the functionaltool, e.g. Image Diagnostic Tool or Scan Diagnostic Tool, and itsassociated functionality may be embodied in a computer system/server ina single location, or in one embodiment, may be configured in a cloudbased system sharing computing resources. With references to FIG. 9, ablock diagram (900) is provided illustrating an example of a computersystem/server (902), hereinafter referred to as a host (902) incommunication with a cloud based support system, to implement theprocesses described above with respect to FIGS. 1-8. Host (902) isoperational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with host (902) include, but are not limited to,personal computer systems, server computer systems, thin clients, thickclients, hand-held or laptop devices, multiprocessor systems,microprocessor-based systems, set top boxes, programmable consumerelectronics, network PCs, minicomputer systems, mainframe computersystems, and file systems (e.g., distributed storage environments anddistributed cloud computing environments) that include any of the abovesystems, devices, and their equivalents.

Host (902) may be described in the general context of computersystem-executable instructions, such as program modules, being executedby a computer system. Generally, program modules may include routines,programs, objects, components, logic, data structures, and so on thatperform particular tasks or implement particular abstract data types.Host (902) may be practiced in distributed cloud computing environmentswhere tasks are performed by remote processing devices that are linkedthrough a communications network. In a distributed cloud computingenvironment, program modules may be located in both local and remotecomputer system storage media including memory storage devices.

As shown in FIG. 9, host (902) is shown in the form of a general-purposecomputing device. The components of host (902) may include, but are notlimited to, one or more processors or processing units (904), a systemmemory (906), and a bus (908) that couples various system componentsincluding system memory (906) to processor (904). Bus (908) representsone or more of any of several types of bus structures, including amemory bus or memory controller, a peripheral bus, an acceleratedgraphics port, and a processor or local bus using any of a variety ofbus architectures. By way of example, and not limitation, sucharchitectures include Industry Standard Architecture (ISA) bus, MicroChannel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus. Host (902) typically includes avariety of computer system readable media. Such media may be anyavailable media that is accessible by host (902) and it includes bothvolatile and non-volatile media, removable and non-removable media.

Memory (906) can include computer system readable media in the form ofvolatile memory, such as random access memory (RAM) (972) and/or cachememory (974). By way of example only, storage system (976) can beprovided for reading from and writing to a non-removable, non-volatilemagnetic media (not shown and typically called a “hard drive”). Althoughnot shown, a magnetic disk drive for reading from and writing to aremovable, non-volatile magnetic disk (e.g., a “floppy disk”), and anoptical disk drive for reading from or writing to a removable,non-volatile optical disk such as a CD-ROM, DVD-ROM or other opticalmedia can be provided. In such instances, each can be connected to bus(908) by one or more data media interfaces.

Program/utility (978), having a set (at least one) of program modules(980), may be stored in memory (906) by way of example, and notlimitation, as well as an operating system, one or more applicationprograms, other program modules, and program data. Each of the operatingsystems, one or more application programs, other program modules, andprogram data or some combination thereof, may include an implementationof a networking environment. Program modules (980) generally carry outthe functions and/or methodologies of embodiments to store and analyzedata. For example, the set of program modules (980) may include themodules configured as an Image Diagnostic Tool in order to interpret aseries of images acquired from a subject over time and provide adiagnosis based on the interpretation as shown and described in FIGS.1-8.

Host (902) may also communicate with one or more external devices (982),such as a keyboard, a pointing device, etc.; a display (984); one ormore devices that enable a user to interact with host (902); and/or anydevices (e.g., network card, modem, etc.) that enable host (902) tocommunicate with one or more other computing devices. Such communicationcan occur via Input/Output (I/O) interface(s) (986). Still yet, host(902) can communicate with one or more networks such as a local areanetwork (LAN), a general wide area network (WAN), and/or a publicnetwork (e.g., the Internet) via network adapter (988). As depicted,network adapter (988) communicates with the other components of host(902) via bus (908). In one embodiment, a plurality of nodes of adistributed file system (not shown) is in communication with the host(902) via the I/O interface (986) or via the network adapter (988). Itshould be understood that although not shown, other hardware and/orsoftware components could be used in conjunction with host (902).Examples, include, but are not limited to: microcode, device drivers,redundant processing units, external disk drive arrays, RAID systems,tape drives, and data archival storage systems, etc.

In this document, the terms “computer program medium,” “computer usablemedium,” and “computer readable medium” are used to generally refer tomedia such as main memory (906), including RAM (972), cache (974), andstorage system (976), such as a removable storage drive and a hard diskinstalled in a hard disk drive.

Computer programs (also called computer control logic) are stored inmemory (906). Computer programs may also be received via a communicationinterface, such as network adapter (988). Such computer programs, whenrun, enable the computer system to perform the features of the presentembodiments as discussed herein. In particular, the computer programs,when run, enable the processing unit (904) to perform the features ofthe computer system. Accordingly, such computer programs representcontrollers of the computer system.

The present embodiments may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent embodiments.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present embodiments may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present embodiments.

In one embodiment, host (902) is a node (990) of a cloud computingenvironment. As is known in the art, cloud computing is a model ofservice delivery for enabling convenient, on-demand network access to ashared pool of configurable computing resources (e.g., networks, networkbandwidth, servers, processing, memory, storage, applications, virtualmachines, and services) that can be rapidly provisioned and releasedwith minimal management effort or interaction with a provider of theservice. This cloud model may include at least five characteristics, atleast three service models, and at least four deployment models. Exampleof such characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based email). Theconsumer does not manage or control the underlying cloud infrastructureincluding network, servers, operating systems, storage, or evenindividual application capabilities, with the possible exception oflimited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting for loadbalancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 10, an illustrative cloud computing network(1000). As shown, cloud computing network (1000) includes a cloudcomputing environment (1050) having one or more cloud computing nodes(1010) with which local computing devices used by cloud consumers maycommunicate. Examples of these local computing devices include, but arenot limited to, personal digital assistant (PDA) or cellular telephone(1055A), desktop computer (1055B), laptop computer (1055C), and/orautomobile computer system (1055N). Individual nodes within nodes (1010)may further communicate with one another. They may be grouped (notshown) physically or virtually, in one or more networks, such asPrivate, Community, Public, or Hybrid clouds as described hereinabove,or a combination thereof. This allows cloud computing environment (1000)to offer infrastructure, platforms and/or software as services for whicha cloud consumer does not need to maintain resources on a localcomputing device. It is understood that the types of computing devices(1055A-N) shown in FIG. 10 are intended to be illustrative only and thatthe cloud computing environment (1050) can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 11, a set of functional abstraction layersprovided by the cloud computing network of FIG. 9 is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 11 are intended to be illustrative only, and the embodiments arenot limited thereto. As depicted, the following layers and correspondingfunctions are provided: hardware and software layer (1110),virtualization layer (1120), management layer (1130), and workload layer(1140). The hardware and software layer (1110) includes hardware andsoftware components. Examples of hardware components include mainframes,in one example IBM® zSeries® systems; RISC (Reduced Instruction SetComputer) architecture based servers, in one example IBM pSeries®systems; IBM xSeries® systems; IBM BladeCenter® systems; storagedevices; networks and networking components. Examples of softwarecomponents include network application server software, in one exampleIBM WebSphere® application server software; and database software, inone example IBM DB2® database software. (IBM, zSeries, pSeries, xSeries,BladeCenter, WebSphere, and DB2 are trademarks of International BusinessMachines Corporation registered in many jurisdictions worldwide).

Virtualization layer (1120) provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers;virtual storage; virtual networks, including virtual private networks;virtual applications and operating systems; and virtual clients.

In one example, management layer (1130) may provide the followingfunctions: resource provisioning, metering and pricing, user portal,service level management, and SLA planning and fulfillment. Resourceprovisioning provides dynamic procurement of computing resources andother resources that are utilized to perform tasks within the cloudcomputing environment. Metering and pricing provides cost tracking asresources are utilized within the cloud computing environment, andbilling or invoicing for consumption of these resources. In one example,these resources may comprise application software licenses. Securityprovides identity verification for cloud consumers and tasks, as well asprotection for data and other resources. User portal provides access tothe cloud computing environment for consumers and system administrators.Service level management provides cloud computing resource allocationand management such that required service levels are met. Service LevelAgreement (SLA) planning and fulfillment provides pre-arrangement for,and procurement of, cloud computing resources for which a futurerequirement is anticipated in accordance with an SLA.

Workloads layer (1140) provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include, but are notlimited to: mapping and navigation; software development and lifecyclemanagement; virtual classroom education delivery; data analyticsprocessing; transaction processing; and interpretation of imagesacquired from a subject over time and determination of a diagnosis basedon the interpretation.

As will be appreciated by one skilled in the art, the aspects may beembodied as a system, method, or computer program product. Accordingly,the aspects may take the form of an entirely hardware embodiment, anentirely software embodiment (including firmware, resident software,micro-code, etc.), or an embodiment combining software and hardwareaspects that may all generally be referred to herein as a “circuit,”“module,” or “system.” Furthermore, the aspects described herein maytake the form of a computer program product embodied in one or morecomputer readable medium(s) having computer readable program codeembodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

The embodiments are described above with reference to flow chartillustrations and/or block diagrams of methods, apparatus (systems), andcomputer program products. It will be understood that each block of theflow chart illustrations and/or block diagrams, and combinations ofblocks in the flow chart illustrations and/or block diagrams, can beimplemented by computer program instructions. These computer programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flow chart and/or block diagram block orblocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flow chart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions, which execute on thecomputer or other programmable apparatus, provide processes forimplementing the functions/acts specified in the flow chart and/or blockdiagram block or blocks.

The flow charts and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments. In this regard, each block in the flow charts or blockdiagrams may represent a module, segment, or portion of code, whichcomprises one or more executable instructions for implementing thespecified logical function(s). It should also be noted that, in somealternative implementations, the functions noted in the block may occurout of the order noted in the figures. For example, two blocks shown insuccession may, in fact, be executed substantially concurrently, or theblocks may sometimes be executed in the reverse order, depending uponthe functionality involved. It will also be noted that each block of theblock diagrams and/or flow chart illustration(s), and combinations ofblocks in the block diagrams and/or flow chart illustration(s), can beimplemented by special purpose hardware-based systems that perform thespecified functions or acts, or combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used herein, thesingular forms “a”, “an” and “the” are intended to include the pluralforms as well, unless the context clearly indicates otherwise. It willbe further understood that the terms “comprises” and/or “comprising,”when used in this specification, specify the presence of statedfeatures, integers, steps, operations, elements, and/or components, butdo not preclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof.

The embodiments described herein may be implemented in a system, amethod, and/or a computer program product. The computer program productmay include a computer readable storage medium (or media) havingcomputer readable program instructions thereon for causing a processorto carry out the embodiments described herein.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmissions, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

The embodiments are described herein with reference to flow chartillustrations and/or block diagrams of methods, apparatus (systems), andcomputer program products. It will be understood that each block of theflow chart illustrations and/or block diagrams, and combinations ofblocks in the flow chart illustrations and/or block diagrams, can beimplemented by computer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flow chart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flow chart and/or block diagram blockor blocks.

It will be appreciated that, although specific embodiments have beendescribed herein for purposes of illustration, various modifications maybe made without departing from the spirit and scope of the specificembodiments described herein. Accordingly, the scope of protection islimited only by the following claims and their equivalents.

Aspects of the present embodiments are described herein with referenceto flowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments. Itwill be understood that each block of the flowchart illustrations and/orblock diagrams, and combinations of blocks in the flowchartillustrations and/or block diagrams, can be implemented by computerreadable program instructions.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present embodiments has been presented for purposesof illustration and description, but is not intended to be exhaustive orlimited to the embodiments in the form disclosed.

Indeed, executable code could be a single instruction, or manyinstructions, and may even be distributed over several different codesegments, among different applications, and across several memorydevices. Similarly, operational data may be identified and illustratedherein within the tool, and may be embodied in any suitable form andorganized within any suitable type of data structure. The operationaldata may be collected as a single dataset, or may be distributed overdifferent locations including over different storage devices, and mayexist, at least partially, as electronic signals on a system or network.

Furthermore, the described features, structures, or characteristics maybe combined in any suitable manner in one or more embodiments. In thefollowing description, numerous specific details are provided, such asexamples of agents, to provide a thorough understanding of the disclosedembodiments. One skilled in the relevant art will recognize, however,that the embodiments can be practiced without one or more of thespecific details, or with other methods, components, materials, etc. Inother instances, well-known structures, materials, or operations are notshown or described in detail to avoid obscuring aspects of theembodiments.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present embodiments have been presented for purposesof illustration and description, but is not intended to be exhaustive orlimited to the embodiments in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the embodiments. Theembodiment was chosen and described in order to best explain theprinciples of the embodiments and the practical application, and toenable others of ordinary skill in the art to understand the embodimentsfor various embodiments with various modifications as are suited to theparticular use contemplated. Accordingly, the implementationinterpreting a series of images acquired from a subject over timeprovides a diagnosis based on the interpretation.

It will be appreciated that, although specific embodiments have beendescribed herein for purposes of illustration, various modifications maybe made without departing from the spirit and scope of the embodiments.In particular, the number of images, points on a graph, data storages,and comparisons should not be considered limiting. In one embodiment,the Image Diagnostic Tool can be located on a different computer systemthan the data storage and the system acquiring the image. Accordingly,the scope of protection of these embodiments is limited only by thefollowing claims and their equivalents.

What is claimed is:
 1. A computer system comprising: a processing unitin communication with a memory; a functional unit in communication withthe processing unit having an image diagnostic tool to determine adiagnosis, the image diagnostic tool to: capture a first image of afirst ovary; recognize a first follicle utilizing a morphologicalfeature of the first follicle including determine on a pixel basis eachmorphological feature utilizing a template set having an instruction fordetermination of one or more morphological features; perform a firstcomparison including compare the first follicle, on a pixel basis, to apreviously recognized follicle of at least one ovary; determine a firstprobability based on the performed first comparison; create a firstdiagnosis based on the performed first comparison and determined firstprobability; and convert the first captured image to a first diagnosticimage, the conversion utilizing the recognized first follicle, createdfirst diagnosis and determined first probability, and the firstdiagnostic image being a visual representation of the created firstdiagnosis.
 2. The system of claim 1, further comprising the imagediagnostic tool to: capture a second image of the first ovary; recognizea second follicle within the second captured image utilizing themorphological feature of the second follicle within the second capturedimage including determine on a pixel basis each morphological featureutilizing the template set; perform a second comparison includingcompare the second follicle of the second captured image, on a pixelbasis, to at least one previously recognized follicle of at least oneovary; determine a second probability based on the performed secondcomparison; create a second diagnosis based on the performed first andsecond comparisons and the determined first and second probabilities;and convert the first and second captured images to a second diagnosticimage, the conversion utilizing the recognized second follicle, createdsecond diagnosis, and determined second probability, and the seconddiagnostic image being a visual representation of the created seconddiagnosis.
 3. The system of claim 2, further comprising the imagediagnostic tool to: determine a change between the recognized first andsecond follicle; perform a third comparison including compare thedetermined change to a previously determined change of at least onefollicle of at least one ovary; determine a third probability based onthe performed third comparison; and create a third diagnosis based onthe performed third comparison and determined third probability; whereinthe conversion of the first and second captured images includesutilizing the created third diagnosis and determined third probability.4. The system of claim 2, further comprising the image diagnostic toolto: recalculate the determined first probability based on the createdsecond diagnosis; create a polyline based on the first and secondcaptured images, and recalculated first and determined secondprobabilities; and create a polyline diagnosis based on the polyline. 5.The system of claim 1, further comprising the image diagnostic tool to:determine one or more parameters of a first subject; and wherein theperformed first comparison includes compare the first follicle to apreviously recognized follicle of at least one previous subject havingone or more similar parameters to the first subject.
 6. The system ofclaim 1, wherein the morphological feature is selected from the groupconsisting of: edge, size, shape, and color.
 7. A computer programproduct for determining a diagnosis, the computer program productcomprising a computer readable storage device having program codeembodied therewith, the program code executable by a processor to:capture a first image of a first ovary; recognize a first follicleutilizing a morphological feature of the first follicle includingdetermine on a pixel basis each morphological feature utilizing atemplate set having an instruction for determination of one or moremorphological features; perform a first comparison including compare thefirst follicle, on a pixel basis, to a previously recognized follicle ofat least one ovary; determine a first probability based on the performedfirst comparison; create a first diagnosis based on the performed firstcomparison and determined first probability; and convert the firstcaptured image to a first diagnostic image, the conversion utilizing therecognized first follicle, created first diagnosis and determined firstprobability, and the first diagnostic image being a visualrepresentation of the created first diagnosis.
 8. The computer programproduct of claim 7, further comprising program code to: capture a secondimage of the first ovary; recognize a second follicle within the secondcaptured image utilizing the morphological feature of the secondfollicle within the second captured image including determine on a pixelbasis each morphological feature utilizing the template set; perform asecond comparison including compare the second follicle of the secondcaptured image, on a pixel basis, to at least one previously recognizedfollicle of at least one ovary; determine a second probability based onthe performed second comparison; create a second diagnosis based on theperformed first and second comparisons and the determined first andsecond probabilities; and convert the first and second captured imagesto a second diagnostic image, the conversion utilizing the recognizedsecond follicle, created second diagnosis, and determined secondprobability, and the second diagnostic image being a visualrepresentation of the created second diagnosis.
 9. The computer programproduct of 8, further comprising program code to: determine a changebetween the recognized first and second follicle; perform a thirdcomparison including compare the determined change to a previouslydetermined change of at least one follicle of at least one ovary;determine a third probability based on the performed third comparison;and create a third diagnosis based on the performed third comparison anddetermined third probability; wherein the conversion of the first andsecond captured images includes utilizing the created third diagnosisand determined third probability.
 10. The computer program product ofclaim 8, further comprising program code to: recalculate the determinedfirst probability based on the created second diagnosis; create apolyline based on the first and second captured images, and recalculatedfirst and determined second probabilities; and create a polylinediagnosis based on the polyline.
 11. The computer program product ofclaim 7, further comprising program code to: determine one or moreparameters of a first subject; and wherein the performed firstcomparison includes compare the first follicle to a previouslyrecognized follicle of at least one previous subject having one or moresimilar parameters to the first subject.
 12. The computer programproduct of claim 7, wherein the morphological feature is selected fromthe group consisting of: edge, size, shape, and color.
 13. A method fordetermining a diagnosis comprising: capturing a first image of a firstovary; recognizing a first follicle utilizing a morphological feature ofthe first follicle including determining on a pixel basis eachmorphological feature utilizing a template set having an instruction fordetermination of one or more morphological features; performing a firstcomparison including comparing the first follicle, on a pixel basis, toa previously recognized follicle of at least one ovary; determining afirst probability based on the performed first comparison; creating afirst diagnosis based on the performed first comparison and determinedfirst probability; and converting the first captured image to a firstdiagnostic image, the converting utilizing the recognized firstfollicle, created first diagnosis and determined first probability, andthe first diagnostic image being a visual representation of the createdfirst diagnosis.
 14. The method of claim 13, further comprising:capturing a second image of the first ovary; recognizing a secondfollicle within the second captured image utilizing the morphologicalfeature of the second follicle within the second captured imageincluding determining on a pixel basis each morphological featureutilizing the template set; performing a second comparison includingcomparing the second follicle of the second captured image, on a pixelbasis, to at least one previously recognized follicle of at least oneovary; determining a second probability based on the performed secondcomparison; creating a second diagnosis based on the performed first andsecond comparisons and the determined first and second probabilities;and converting the first and second captured images to a seconddiagnostic image, the converting utilizing the recognized secondfollicle, created second diagnosis, and determined second probability,and the second diagnostic image being a visual representation of thecreated second diagnosis.
 15. The method of 14, further comprising:determining a change between the recognized first and second follicle;performing a third comparison including comparing the determined changeto a previously determined change of at least one follicle of at leastone ovary; determining a third probability based on the performed thirdcomparison; and creating a third diagnosis based on the performed thirdcomparison and determined third probability; wherein the converting thefirst and second captured images includes utilizing the created thirddiagnosis and determined third probability.
 16. The method of claim 14,further comprising: recalculating the determined first probability basedon the created second diagnosis; creating a polyline based on the firstand second captured images, and recalculated first and determined secondprobabilities; and creating a polyline diagnosis based on the polyline.17. The method of claim 13, further comprising: determining one or moreparameters of a first subject; and wherein the performed firstcomparison includes comparing the first follicle to a previouslyrecognized follicle of at least one previous subject having one or moresimilar parameters to the first subject.
 18. The method of claim 13,wherein the morphological feature is selected from the group consistingof: edge, size, shape, and color.